# negotiator402 Negotiation Session

> negotiator402 Negotiation Session is a paid API for AI agents from negotiator402.alogos.xyz, paid per call via x402, $10/call, status unknown (last checked 2026-09-15).

Takes a plain-language 2-party negotiation scenario (context, objectives, constraints) and returns a structured negotiation plan with ZOPA, BATNA, concession schedule, decision points, and scenario branches — with LLM-derived guidance flagged as unverified.

## Facts

- Endpoint: POST https://negotiator402.alogos.xyz/v1/negotiation-session
- Price: $10/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/negotiator402-negotiation-session-d83966d6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_rFNq1Zf_kpAVGwdnNbTQ3

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability negotiator402-negotiation-session-d83966d6 -d '<json body>'
```

Example prompt: I need a full negotiation strategy for a software licensing deal — my objective is to get the annual price under $50K with a 3-year contract, my constraints are a hard budget cap of $60K and I can't accept anything shorter than 2 years, and I know the vendor tends to anchor high but values long-term commitments. Can you give me a concession schedule, decision points, and ZOPA analysis?

## When to prefer this

Use this endpoint when you need a structured, multi-step negotiation plan with concession schedules and scenario branches for a 2-party deal expressed in plain language — especially when you want ZOPA and BATNA framing. Prefer it over a raw LLM prompt when you want a standardized JSON output with explicit verification flags distinguishing solver-backed from inferred results, and when pay-per-call (no subscription) is preferred. For pure game-theory Nash equilibrium or Vickrey auction problems, sibling endpoints on the same host may be more appropriate.

## Known failure modes

- verified: 'refused' returned if the negotiation scenario cannot be formulated or solved
- Malformed input (missing required fields: context, objectives, or constraints) returns a 4xx error
- Ambiguous or extremely sparse context may produce low-quality LLM-derived guidance with no solver-backed components
- Payment failure via x402/MPP blocks the call entirely
- Truncated response schema suggests some output fields may be absent in edge cases

## How this service works

Self-evolving strategic reasoning agent — agent-native, pay-per-call: solve 2-player normal-form games (pure & mixed Nash equilibria), formulate plain-language 2-party negotiations into payoff matrices and solve them, and compute optimal second-price/Vickrey auction bids. Unlike a raw LLM endpoint, every answer carries a `verified` trust flag — `verified:true` means a deterministic solver (nashpy / closed-form) computed and confirmed the result, NOT a language model. The formulator layer self-improves training-free from production traffic: every formulation is logged, solver-verified outcomes become reward signals, and the underlying LoRA model is periodically retrained (SFT + RLVR) on its own verified history. No API key, no subscription — agents pay per request via x402 (Base/USDC) or MPP (Tempo/pathUSD).

## Output

A structured JSON object containing: a `guidance` block with ZOPA, BATNA, reservation and aspiration values (LLM-derived, inferred); an `offer_plan` with a sequenced offer/counteroffer list, concession schedule, decision points, and scenario branches; a narrative `explanation`; and a `verification` block indicating which components (if any) were solver-verified. The `verified` field will be 'false' for successful sessions (full artifact is LLM-derived, not deterministically verified) or 'refused' on failure. Cost is $10 USDC per call via x402 (Base/USDC) or MPP.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "context",
  "objectives",
  "constraints"
 ],
 "properties": {
  "context": {
   "type": "string",
   "description": "Description of the negotiation context and background."
  },
  "objectives": {
   "type": "string",
   "description": "Your objectives and desired outcomes."
  },
  "constraints": {
   "type": "string",
   "description": "Your constraints: budget, timeline, red lines, walk-away conditions."
  },
  "offer_history": {
   "type": "string",
   "description": "Optional: previous offers/exchanges so far."
  },
  "counterpart_profile": {
   "type": "string",
   "description": "Optional: known information about the counterpart (style, preferences, history)."
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "verified",
  "guidance",
  "concession_schedule",
  "decision_points",
  "scenario_branches",
  "verification"
 ],
 "properties": {
  "guidance": {
   "type": "object",
   "properties": {
    "zopa": {
     "type": "object",
     "description": "Zone of Possible Agreement. Solver-backed when computable."
    },
    "batna": {
     "type": [
      "number",
      "string",
      "null"
     ],
     "description": "LLM-derived BATNA/walk-away guidance."
    },
    "source": {
     "enum": [
      "llm_derived"
     ],
     "type": "string"
    },
    "target": {
     "type": "number",
     "description": "Target outcome. Inferred guidance."
    },
    "inferred": {
     "type": "boolean",
     "description": "Always true for session guidance."
    },
    "aspiration": {
     "type": "number",
     "description": "Aspiration / stretch goal. Inferred guidance."
    },
    "reservation": {
     "type": [
      "number",
      "string",
      "null"
     ],
     "description": "LLM-derived reservation guidance."
    }
   },
   "additionalProperties": true
  },
  "verified": {
   "enum": [
    "false",
    "refused"
   ],
   "type": "string",
   "description": "Always 'false' for a successful session because the complete LLM-derived artifact is not deterministically verified; 'refused' on failure."
  },
  "offer_plan": {
   "type": "object",
   "properties": {
    "decision_points": {
     "type": "array",
     "items": {
      "type": "object"
     },
     "description": "Key decision forks with conditions."
    },
    "sequenced_offers": {
     "type": "array",
     "items": {
      "type": "object"
     },
     "description": "Ordered offer/counteroffer sequence."
    },
    "scenario_branches": {
     "type": "array",
     "items": {
      "type": "object"
     },
     "description": "Alternative scenarios and responses."
    },
    "concession_schedule": {
     "type": "array",
     "items": {
      "type": "object"
     },
     "description": "Planned concession timing and magnitude."
    }
   },
   "additionalProperties": true
  },
  "explanation": {
   "type": "string",
   "description": "Narrative summary of the negotiation plan. Inferred, not solver-backed."
  },
  "verification": {
   "type": "object",
   "required": [
    "artifact_verified",
    "verified_component_count",
    "component_count",
    "verified_components",
    "components"
   ],
   "properties": {
    "components": {
     "type": "array",
     "i
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/negotiator402-negotiation-session-d83966d6/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from negotiator402.alogos.xyz](https://www.zero.xyz/host/negotiator402.alogos.xyz/llms.txt)